Frequency-based horizon interpretation based on seismic data

ABSTRACT

Seismic data obtained from a seismic survey conducted of a subterranean region is received. The seismic data includes multiple frequency components, having a frequency bandwidth. A target, to-be-picked horizon is identified by displaying well data on the seismic section and correlating the seismic reflector with layer tops in the well data. A horizon represents a seismic reflector between two geological layers in the subterranean region. A single frequency that gives rise to a predetermined continuity along the target horizon is determined from the frequency bandwidth of the seismic data. The seismic data is filtered to mono-frequency volumes. The mono-frequency seismic volumes include a single frequency component. A horizon is picked corresponding to the target horizon based on the mono-frequency volumes. The identified horizon corresponding to the target horizon is output for determining geological features of the subterranean region based on the identified horizon.

BACKGROUND

Horizons refer to surfaces or seismic reflectors that separate differentrock layers in depositional environments. For example, a horizon can bea boundary between two different geological layers in a subterraneanregion. Horizon picking or tracking refers to a process of identifyingor determining a seismic reflector between two geological layers.Horizon picking can be performed manually or automatically. For example,software tools can execute auto horizon picking algorithms to performhorizon picking automatically based on seismic data. Both manual horizonpicking and auto horizon picking can be difficult, time consuming, anderror-prone because of the complexity of the geology, poor signal tonoise ratio of the seismic data, or both.

SUMMARY

The present disclosure describes frequency-based horizon interpretationbased on seismic data.

In an implementation, seismic data obtained from a seismic surveyconducted of a subterranean region is received. The seismic dataincludes multiple frequency components, having a frequency bandwidth. Atarget to-be-picked horizon is identified based on the seismic data. Ahorizon represents a seismic reflector between two geological layers inthe subterranean region. A single frequency that gives rise to apredetermined continuity along the target horizon is determined from thefrequency bandwidth of the seismic data. The seismic data is filtered tomono-frequency volumes. The mono-frequency volumes include a singlefrequency component. A horizon is identified by displaying well data onthe seismic section and correlating the seismic reflector with layertops in the well data. The identified and picked horizon is used fordetermining geological features of the subterranean region.

In a second implementation, a non-transitory, computer-readable mediumstores one or more instructions executable by a computer system toperform the following operations. Seismic data of a subterranean regionis received. The seismic data includes multiple frequency components andhas a frequency bandwidth spanning a spectrum in a frequency domain. Theseismic data is obtained from a seismic survey conducted on thesubterranean region. A target horizon is identified based on the seismicdata. The target horizon is a horizon to be picked based on the seismicdata. The horizon represents a seismic reflector between two geologicallayers in the subterranean region. A single frequency is determined fromthe frequency bandwidth of the seismic data that gives rise to apredetermined continuity along the target horizon. The seismic dataincludes multiple frequency components, which are filtered tomono-frequency seismic data at the single frequency. The mono-frequencyseismic data includes a single-frequency component at the singlefrequency. A horizon corresponding to the target horizon is picked basedon the mono-frequency seismic data. The identified horizon correspondingto the target horizon is output for determining geological features ofthe subterranean region based on the identified horizon.

In a third implementation, a computer-implemented system includes one ormore computers and one or more computer memory devices interoperablycoupled with the one or more computers. The one or more computer memorydevices have tangible, non-transitory, machine-readable media storingone or more instructions. The one or more instructions are executable bya computer system to perform the following operations. Seismic data of asubterranean region is received. The seismic data includes multiplefrequency components and has a frequency bandwidth spanning a spectrumin a frequency domain. The seismic data is obtained from a seismicsurvey conducted on the subterranean region. A target horizon isidentified based on the seismic data. The target horizon is a horizon tobe picked based on the seismic data. The horizon represents a seismicreflector between two geological layers in the subterranean region. Asingle frequency is determined from the frequency bandwidth of theseismic data that gives rise to a predetermined continuity along thetarget horizon. The seismic data includes multiple frequency components,which are filtered to mono-frequency seismic data at the singlefrequency. The mono-frequency seismic data includes a single-frequencycomponent at the single frequency. A horizon corresponding to the targethorizon is picked based on the mono-frequency seismic data. Theidentified horizon corresponding to the target horizon is output fordetermining geological features of the subterranean region based on theidentified horizon.

The foregoing and other described implementations can each, optionally,include one or more of the following features.

As a first feature, combinable with any of the following features,determining a single frequency from the frequency bandwidth of theseismic data that gives rise to a predetermined continuity along thetarget horizon include one or more of the following operations. Theseismic data is decomposed into a number of mono-frequency seismic dataat a number of frequencies. A number of respective horizons are pickedbased on the number of mono-frequency seismic data. Out of the number ofrespective horizons, a horizon that has the predetermined continuityalong the target horizon is determined. The single frequency that givesrise to the horizon that has the predetermined continuity along thetarget horizon is determined.

As a second feature, combinable with any of the previous or followingfeatures, the horizon that has the predetermined continuity along thetarget horizon includes a horizon that has a maximum continuity alongthe target horizon among the number of respective horizons.

As a third feature, combinable with any of the previous or followingfeatures, outputting the identified horizon includes displaying theidentified horizon on an image of the seismic data.

As a fourth feature, combinable with any of the previous or followingfeatures, identifying a target horizon based on the seismic dataincludes receiving a user's selection of the target horizon based on theseismic data.

As a fifth feature, combinable with any of the previous or followingfeatures, the method the operations include determining geologicalfeatures of the subterranean region based on the identified horizon.

As a sixth feature, combinable with any of the previous or followingfeatures, determining geological features of the subterranean regionbased on the identified horizon includes estimating a thickness of ageological layer between the identified horizon and another horizon ofthe subterranean region.

Implementations of the described subject matter, including thepreviously described implementation, can be implemented using acomputer-implemented method, a non-transitory, computer-readable mediumor a computer-implemented system. The medium can store computer-readableinstructions to perform the computer-implemented method. The system caninclude one or more computer memory devices interoperably coupled withone or more computers and with the computer-readable medium.

The subject matter described in this specification can be implemented inparticular implementations, so as to realize one or more of thefollowing advantages. First, the described subject matter can improvethe auto horizon picking process by providing more reliable andconsistent horizon picks. Second, the described subject matter canaccelerate the process of horizon picking while enhancing the quality ofthe horizon picks. Other advantages will be apparent to those ofordinary skill in the art.

The details of one or more implementations of the subject matter of thisspecification are set forth in the description, the claims, and theaccompanying drawings. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the claims,and the accompanying drawings.

DESCRIPTION OF DRAWINGS

FIG. 1A is a diagram illustrating an example seismic survey system and acomputer system for frequency-based horizon interpretation based onseismic data, according to an implementation of the present disclosure.

FIG. 1B is a flowchart illustrating an example of a method forfrequency-based horizon interpretation based on seismic data, accordingto an implementation of the present disclosure.

FIG. 2 is a data plot illustrating an example result of auto horizonpicking performed on an original seismic volume with a full bandwidth offrequencies, according to an implementation of the present disclosure.

FIG. 3 is a data plot illustrating an example result of auto horizonpicking performed on a 10 hertz (Hz) filtered seismic volume, accordingto an implementation of the present disclosure.

FIG. 4 is a data plot illustrating an example result of auto horizonpicking performed on a 20 Hz filtered seismic volume, according to animplementation of the present disclosure.

FIG. 5 is a data plot illustrating an example result of auto horizonpicking performed on a 30 Hz filtered seismic volume, according to animplementation of the present disclosure.

FIG. 6 is a data plot illustrating an example result of auto horizonpicking performed on a 40 Hz filtered seismic volume, according to animplementation of the present disclosure.

FIG. 7 is a data plot illustrating an example result of auto horizonpicking performed on a 50 Hz filtered seismic volume, according to animplementation of the present disclosure.

FIG. 8 is a data plot illustrating an example result of auto horizonpicking performed on a 60 Hz filtered seismic volume, according to animplementation of the present disclosure.

FIG. 9 is a block diagram illustrating an example of a computer systemused to provide computational functionalities associated with describedalgorithms, methods, functions, processes, flows, and procedures,according to an implementation of the present disclosure.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

Seismic data contains information about various geological features.Seismic data can be obtained from seismic surveys to image geologicalstructures of a subterranean region. Seismic data can includetwo-dimensional (2D) seismic slices, three-dimensional (3D) seismicvolumes, or higher dimensional data to reflect geologic structural andstratigraphic features such as subsurface faults and unconformities thatare commonly associated with hydrocarbon discoveries.

Typically, seismic data can have a frequency bandwidth that ranges from10 Hz to 80 Hz. The seismic data components at different frequenciescontain information about various geological features. Exampletechniques of frequency-based horizon interpretation based on seismicdata are disclosed. Horizon interpretation can include horizon picking,as well as the processing of the picked horizon for deriving additionalinformation of a subterranean region. As described previously, horizonpicking is also referred to as horizon tracking. Horizon picking refersto a process of determining, selecting, or otherwise identifying ahorizon that represents a seismic reflector between two geologicallayers.

Unlike conventional techniques that perform horizon picking on fullstack seismic volumes and use the full bandwidth of frequencies presentin the seismic data, the described techniques perform horizon pickingbased on mono-frequency seismic data (or single-frequency seismic data).For example, conventional horizon interpretations are performed on thefull stack volumes that contain the full bandwidth of frequenciesranging from 10 Hz to 80 Hz. By contrast, the described techniques useseismic volumes that are filtered to a single frequency and then performthe horizon picking on the filtered single-frequency seismic volumes.

In some implementations, continuity features of layer reflectors inseismic data can be better observed at single frequency volumes ratherthan full stack seismic volumes. The continuity can refer to a lateraldistance from a specified location and in a specified azimuthaldirection for which a reflection character of a seismic event isessentially unchanged. In some implementations, using mono-frequencyseismic data can help reduce or remove noise created by differentsources that occurs at certain specific frequencies. Similarly, usingmono-frequency seismic data can help reduce or remove effects ofmultiples generated from shallow reflectors that would affect an imageof a deeper geological reflector. For example, the imaging of specificgeological features, such as a boundary between two geological layers,can be improved at a specific mono-frequency and the accuracy of horizonpicking can be enhanced by using mono-frequency seismic data.

In some implementations, the described techniques of frequency-basedhorizon interpretation can perform horizon interpretation based onmono-frequency seismic data. In some implementations, the seismic datainclude 3D seismic volumes. The 3D seismic volumes can be full stackseismic volumes that span the full bandwidth of the seismic data (forexample, from 10 Hz to 80 Hz). In some implementations, for thefrequency-based horizon interpretation, a mono frequency or a singlefrequency that provides the largest continuity along a targeted horizoncan be determined. A mono-frequency seismic data can be generated byfiltering an original seismic volume (for example, the full stackseismic volumes) to the single frequency. For example, themono-frequency seismic data can include mono-frequency volumes thatinclude a single frequency component of the seismic data. A horizon canbe picked on the filtered mono-frequency seismic data. The pickedhorizon can then be displayed on the full stack seismic volume.

Compared to conventional techniques that are labor intensive and mayfail when there is a change in the reflector continuity, which is oftenpresent in the full stack seismic volumes, the described techniques canprovide a faster and more reliable method for horizon picking. In someimplementations, the described techniques can produce a mono-frequencyseismic volume that provides a continuous horizon where the horizon issmooth, thus facilitating horizon picking with a minimum noise impact.

In some implementations, the described techniques can improvereliability of horizon picking and decrease risk in hydrocarbonexploration by a more effective prospect generation process. Forexample, the described techniques can reduce uncertainties in reservoirdepth estimation and improve discovery by mapping different geologicalfeatures and locating potential areas for drilling. In someimplementations, the described techniques can complement and improve theconventional techniques.

In some implementations, the output of the described techniques can beprovided as a report that includes picked or identified seismic horizonsfrom the seismic data. The output of the described techniques can alsoinclude interpretations of the horizons. For example, the describedtechniques can be implemented as a module that is internal or externalto a seismic interpretation system. The described techniques candecompose the seismic data into different frequency components for anauto picker (for example, an automated horizon picking module in theseismic interpretation system) to work effectively, which savesinterpretation time and improves quality.

In some implementations, the seismic interpretation system can use theidentified seismic horizons to generate maps and attributes forestimating the thickness of geological layers and evaluating thefeasibility of any hydrocarbon discovery of the subterranean region.Additionally, the output of the described techniques can be used toestimate the hydrocarbon reserve of a reservoir, locate drillinglocations of the subterranean region, and so on. In someimplementations, the identified seismic horizons and other interpretedfeatures of the subterranean region can be displayed on the stackseismic volumes, for example, for the review and analysis of seismicdata interpreters or experts.

FIG. 1A is a diagram showing an example seismic survey system 100 and acomputer system 155 for frequency-based horizon interpretation based onseismic data, according to an implementation of the present disclosure.The example seismic survey system 100 can be used to acquire seismicdata, and the computer system 155 can receive the seismic data andperform frequency-based horizon interpretation based on the seismicdata, for example, according to an example method 150 shown in FIG. 1B.In the illustrated example, the seismic survey system 100 is used foracquiring seismic data of a subterranean region 114. A seismic surveysystem can be implemented on land, offshore, or in another subterraneanregion.

The computer system 155 can include one or more computing devices orsystems. The computer system 155 or any of its components can be locatedapart from the other components shown in FIG. 1A. For example, thecomputer system 155 can be located at a data processing center, acomputing facility, or another suitable location. The seismic surveysystem 100 can include additional or different features, and thefeatures of the seismic survey system can be arranged as shown in FIG.1A or in another configuration.

The example seismic survey system 100 includes a source vehicle 101 (forexample, a truck or a ship) carrying navigation equipment and an energysource 102 (for example, a seismic air-gun). Multiple receivers 120 (forexample, geophones, hydrophones, or a combination of them) can bedeployed on or near the ground or sea surface 103. The energy source 102can generate waves 130 (for example, in the form of sound or acousticwaves) beneath the ground or sea surface 103. The waves 130 can travelin the subterranean region 114. The waves 130 can be reflected by aseismic reflector 135 (for example, a layer boundary) and recorded bythe receivers 120. In this manner, subsurface sedimentary structuresthat trap oil such as faults, folds, and domes, can be mapped by thereflected waves 130. The amplitude, phase, frequency, and travel timeinformation of the waves 130 at specified (x, y, z) locations within thesubterranean region 114 can be recorded as seismic data. For example,the frequency information of the seismic data can be used for horizoninterpretation, according to the techniques described in thisdisclosure.

FIG. 1B is a flowchart illustrating an example of a method 150 forfrequency-based horizon interpretation based on seismic data accordingto an implementation of the present disclosure. For clarity ofpresentation, the description that follows generally describes method150 in the context of the other figures in this description. However, itwill be understood that method 150 can be performed, for example, by anysystem, environment, software, and hardware, or a combination ofsystems, environments, software, and hardware, as appropriate. In someimplementations, various steps of method 150 can be run in parallel, incombination, in loops, or in any order. In some implementations, varioussteps of method 150 can be performed manually or automatically, forexample, by data processing apparatus.

At 151, seismic data of a subterranean region is received. The seismicdata can be received by a data processing apparatus (for example, one ormore processor(s) of the computer system 155 in FIG. 1A or a computersystem 900 in FIG. 9). In some implementations, the seismic data can bestored in a computer-readable medium (for example, memory) and the dataprocessing apparatus can load, retrieve, or otherwise receive theseismic data from the computer-readable medium. The seismic data can becollected, for example, by receivers during a seismic survey conductedof the subterranean region. The subterranean region can include one ormore of a rock formation, a reservoir, or other areas of interest underthe earth's surface (for example, a sea surface or ground surface). Forexample, as described with respect to FIG. 1A, the seismic survey canuse a seismic wave source to generate seismic waves (for example, waves130). Each wave transmitting in the subterranean region can be reflectedby one or more reflectors (for example, subsurfaces in the subterraneanregion) and detected by one or more seismic receivers (for example,geophones) that record seismic waves.

Rather than having a single frequency, the seismic data typicallyincludes multiple frequency components and has a bandwidth spanning aspectrum or a range in a frequency domain. For example, the recordedseismic data can have a bandwidth spanning 10-80 Hz in the frequencydomain.

The seismic data can include 2D, 3D, or higher dimensional seismic data.For example, the seismic data can include 3D seismic volumes. Each 3Dseismic volume can include sets of seismic traces organized into 2Dseismic lines, where each trace has respective x and y coordinates andeach data point of the trace corresponds to a certain seismic traveltime or depth (t or z). In some implementations, the describedtechniques can be applied to 2D seismic data such as the 2D seismiclines. In some implementations, the seismic data can include full stackseismic volumes that span the full bandwidth of the seismic data (forexample, from 10 Hz to 80 Hz). From 151, method 150 proceeds to 152.

At 152, a target horizon based on the seismic data is identified. Thetarget horizon is a horizon to be picked based on the seismic data. Ahorizon can represent a seismic reflector between two geological layersin the subterranean region. The target horizon can be identified by thedata processing apparatus. In some implementations, the data processingapparatus identifies a target horizon by receiving, determining, orotherwise identifying a selection of the target horizon (also referredto as a to-be-picked or to-be-identified horizon). The selection of thetarget horizon can be performed manually by a user (for example, aseismic data interpreter) or automatically by the data processingapparatus. The selection can depend on many factors, such as a zone ofinterest. For example, identifying the target horizon based on theseismic data can include receiving a user's selection of the targethorizon based on the seismic data.

In some implementations, the data processing apparatus can render agraphic user interface (GUI) on a display device of a computer systemfor displaying an image of the seismic data. The user can review theimage of the seismic data and select the target horizon through userinteraction with the image of the seismic data. For example, the user'sselection of the target horizon can include the user's click, touch,gesture, or placement of a cursor or any other user interaction with theimage of the seismic data through the GUI. In some implementations, theuser's selection of the target horizon can include a text input ofcoordinates of an area of interest in the subterranean region of theseismic data. In some implementations, the target horizon can beselected by displaying well data on the seismic section and correlatingthe seismic reflector with layer tops in the well data. For example,during a drilling process of a well, formation tops can be identifiedbased on factors, such as cuttings collected at the well locations,wireline logs, and regional geological studies. The formation topsindicate the beginning of geological layers or bodies. The formationtops are equivalent to the seismic horizons but only at the welllocation. In some implementations, the wireline logs of the wells andthe identified formation tops on the wells are correlated with a seismicsection (one of the seismic lines from the seismic volume that passesthrough or near the well location). Then, the seismic horizon can becorrelated to the formation tops, for example, based on geology to mapeach seismic reflector to a geological body or feature. Then, thehorizon can be identified or picked manually by the user orautomatically by the data processing apparatus. In some implementations,the target horizon can be a point, a line segment, or an area that is apart of the ultimately identified horizon, or approximate to theultimately identified horizon. From 152, method 150 proceeds to 154.

At 154, from the frequency bandwidth of the seismic data, a singlefrequency that gives rise to a predetermined continuity along the targethorizon is determined. In some implementations, the single frequencythat gives rise to the predetermined continuity along the target horizoncan be determined based on empirical knowledge, past experiments, or asearch among different frequencies within the frequency bandwidth of theseismic data. In some implementations, the single frequency can bedetermined automatically by the data processing apparatus. For example,in determining the single frequency, the data processing apparatusdecomposes the seismic data into a number of mono-frequency seismic dataat a number of frequencies. For each of the number of mono-frequencyseismic data, the data processing apparatus identifies, determines,selects, or otherwise picks a respective horizon based on themono-frequency seismic data. As a result, a number of respectivehorizons are identified corresponding to the number of mono-frequencyseismic data. The data processing apparatus then determines out of thenumber of respective horizons a horizon that has the predeterminedcontinuity along the target horizon. The data processing apparatus thendetermines the single frequency that gives rise to the horizon that hasthe predetermined continuity along the target horizon. In someimplementations, the horizon that has the predetermined continuity alongthe target horizon includes a horizon that has a maximum continuityalong the target horizon among the number of respective horizons. Insome implementations, the horizon that has the predetermined continuityalong the target horizon includes a horizon that has a continuity alongthe target horizon exceeding a predetermined threshold. Additional ordifferent criteria can be defined for the horizon that has thepredetermined continuity along the target horizon.

As an example of the search-based method for determining the singlefrequency, consider the seismic data with a frequency bandwidth rangingfrom 10 Hz to 60 Hz. A search step of 5 Hz, 10 Hz, or similar values canbe used by the data processing apparatus to find the single frequencythat gives rise to the predetermined continuity along the targethorizon. In some implementations, the search can start by selecting orotherwise identifying a representative 2D seismic line (for example, 2Dseismic lines 215, 315, 415, 515, 615, 715. and 815 in FIGS. 2-8,respectively) by the data processing apparatus. As described previously,the data processing apparatus can decompose the seismic data intomono-frequencies, assess the horizon continuity level at each frequency,and select the frequency that gives rise to the predeterminedcontinuity. An example process of determining a frequency that gives thelargest continuity along the target horizon is described with referenceto FIGS. 3-8.

In the following, horizon picking using full stack seismic volume thatcontains the full bandwidth of frequencies is described with referenceto FIG. 2, where a cross section of seismic data with a full stack offrequencies is shown. Horizon picking using different mono-frequencyseismic volumes is described with reference to FIGS. 3-8, in each ofwhich a cross section of the seismic data with a respectivemono-frequency is shown. Each time, a single target horizon at the samelocation is selected (for example, by a user) in the respective seismicvolumes of FIGS. 2-8. In response to the selection of the targethorizon, a horizon (for example, a whole horizon in the 3D volume)corresponding to the selected target horizon can be identified, forexample, manually by a user or automatically by the data processingapparatus executing an auto-picker or auto-tracker tool (for example, amodule available in seismic interpretation software).

As an example of manual horizon picking, for each line in the 3D seismicvolume, a user can review and interact with an image of the 3D seismicvolume that is represented on a GUI. In some implementations, the userplaces a cursor on a target horizon and clicks on points along thetarget horizon on the image of the 3D seismic volume. The clicked pointscan be connected together to form the manually picked horizon.

As an example of automatic horizon picking, the data processingapparatus executing an auto-picker (for example, a 2D or a 3D autopicker) can automatically identify multiple points in differentlocations near or along the target horizon to form an automaticallypicked horizon. In some implementations, the auto picker performs anauto picking algorithm (for example, an optimization algorithm) forhorizon picking. The auto picking algorithm can receive a specificlocation of a seismic trace (for example, a location of the targethorizon selected by the user based on the coordinates of the seismictrace) as an input, and search for same or similar features in theadjacent seismic traces. The adjacent seismic traces with the same orsimilar features can be identified, collected, and then output by theauto picking algorithm as the picked horizon. In some instances, thefeatures are not exactly the same along the actual horizon due to noiseor multiples. As such, the auto picking algorithm may not be able topick a location along the actual horizon or may pick a location in thetrace that is not geologically correct. In the case of a 3D auto picker,the data processing apparatus executing the 3D auto picking algorithmcan identify the horizon as a 3D surface. In some implementations, the3D auto picker can reuse the functionalities of the 2D auto picker. Forexample, the 3D auto picker can perform the 2D auto picking algorithm toidentify a 2D horizon for each 2D seismic line in the 3D seismic volume.The multiple identified 2D horizons for the multiple 2D seismic lines inthe 3D seismic volume can form a 3D horizon surface and return as theidentified 3D horizon picked by the 3D auto picker. In someimplementations, the identified 3D horizon can be displayed through theGUI for further error check and fixing, for example, by the user. Insome implementations, the time required to fix those errors can be long.

The proposed method can help address the problems and improve theaccuracy and speed of horizon picking. In some implementations, theseismic volume (for example, a full stack seismic volume) is filtered atdifferent single frequencies so that multiple seismic volumes, each ofwhich is with a single frequency, are generated. Then a seismic line(for example, line 215) is selected from the seismic volume (the sameline in all single-frequency seismic volumes), for example, by a singleclick by a user in the exact location on the horizon in eachsingle-frequency seismic volume (for example, line 215, 315, 415, 515,615, 715, or 815 in FIGS. 2-8, respectively). The segments 210, 310,410, 510, 610, 710, and 810 shown in FIGS. 2-8 are horizons identifiedby the auto picker. The bigger the segment along the horizon the better.

FIG. 2 is a data plot illustrating an example result 200 of auto horizonpicking performed on an original seismic volume 205 with a fullbandwidth of frequencies, according to an implementation of the presentdisclosure. The original seismic volume 205 includes seismic data of asubterranean region, which has a bandwidth ranging from 10-60 Hz. Theoriginal seismic volume 205 is displayed in the data plot as shown inFIG. 2, for example, through a GUI rendered by a computer system. A 2Dauto-tracker is used to perform the auto horizon picking algorithm onthe original seismic volume 205 with the full bandwidth of frequencies.The auto-tracker receives a selection of a target horizon 215. Thetarget horizon 215 can reflect a reflector in the subterranean region.The target horizon 215 can be selected by a user through an interactionwith the GUI, for example, through a single click in an area of intereston the displayed original seismic volume 205. In response to receivingthe target horizon 215, the auto-tracker performs an auto-pickingalgorithm and picks a horizon 210 corresponding to the target horizon215. The identified horizon 210 has a small segment near the location onthe target horizon 215.

FIG. 3 is a data plot illustrating an example result 300 of auto horizonpicking performed on a 10 Hz filtered seismic volume 305, according toan implementation of the present disclosure. The 10 Hz filtered seismicvolume 305 can be obtained by filtering the original seismic volume 205used in FIG. 2 at a single frequency of 10 Hz. The 10 Hz filteredseismic volume 305 is displayed in the data plot as shown in FIG. 3, forexample, through the GUI rendered by the computer system. The targethorizon 215 of the original seismic volume 205 shown in FIG. 2 has acorresponding location 305 on the 10 Hz filtered seismic volume 305shown in in FIG. 3. The 2D auto-tracker is used to pick a horizon 310corresponding to the location 315 that corresponds to the target horizon215 in FIG. 2. In the example shown in FIG. 3, the identified horizon310 has a considerably longer segment compared to the horizon 210 inFIG. 2.

FIG. 4 is a data plot illustrating an example result 400 of auto horizonpicking performed on a 20 Hz filtered seismic volume 405, according toan implementation of the present disclosure. The 20 Hz filtered seismicvolume 405 can be obtained by filtering the original seismic volume 205used in FIG. 2 at a single frequency of 20 Hz. The 20 Hz filteredseismic volume 405 is displayed in the data plot as shown in FIG. 4, forexample, through the GUI rendered by the computer system. The 2Dauto-tracker is used to pick a horizon 410 corresponding to a location415 that corresponds to the target horizon 215 in FIG. 2. In the exampleshown in FIG. 4, the identified horizon 410 is even longer than thehorizon 310 in FIG. 3.

FIG. 5 is a data plot illustrating an example result 500 of auto horizonpicking performed on a 30 Hz filtered seismic volume 505, according toan implementation of the present disclosure. The 30 Hz filtered seismicvolume 505 can be obtained by filtering the original seismic volume 205used in FIG. 2 at a single frequency of 30 Hz. The 30 Hz filteredseismic volume 505 is displayed in the data plot as shown in FIG. 5, forexample, through the GUI rendered by the computer system. The 2Dauto-tracker is used to pick a horizon 510 corresponding to a location515 that corresponds to the target horizon 215 in FIG. 2. In the exampleshown in FIG. 5, the identified horizon 510 is shorter, but with moredetail than the identified horizon 410 in FIG. 4.

FIG. 6 is a data plot illustrating an example result 600 of auto horizonpicking performed on a 40 Hz filtered seismic volume 605, according toan implementation of the present disclosure. The 40 Hz filtered seismicvolume 605 can be obtained by filtering the original seismic volume 205used in FIG. 2 at a single frequency of 40 Hz. The 40 Hz filteredseismic volume 605 is displayed in the data plot as shown in FIG. 6, forexample, through the GUI rendered by the computer system. The 2Dauto-tracker is used to pick a horizon 610 corresponding to a location615 that corresponds to the target horizon 215 in FIG. 2. In the exampleshown in FIG. 6, the identified horizon 610 is significantly shorterthan the identified horizon 410 in FIG. 4.

FIG. 7 is a data plot illustrating an example result 700 of auto horizonpicking performed on a 50 Hz filtered seismic volume 705, according toan implementation of the present disclosure. The 50 Hz filtered seismicvolume 705 can be obtained by filtering the original seismic volume 205used in FIG. 2 at a single frequency of 50 Hz. The 50 Hz filteredseismic volume 705 is displayed in the data plot as shown in FIG. 7, forexample, through the GUI rendered by the computer system. The 2Dauto-tracker is used to pick a horizon 710 corresponding to a location715 that corresponds to the target horizon 215 in FIG. 2. In the exampleshown in FIG. 7, the identified horizon 710 is significantly shorterthan the identified horizon 410 in FIG. 4.

FIG. 8 is a data plot illustrating an example result 800 of auto horizonpicking performed on a 60 Hz filtered seismic volume 805, according toan implementation of the present disclosure. The 60 Hz filtered seismicvolume 805 can be obtained by filtering the original seismic volume 205used in FIG. 2 at a single frequency of 60 Hz. The 60 Hz filteredseismic volume 805 is displayed in the data plot as shown in FIG. 8, forexample, through the GUI rendered by the computer system. The 2Dauto-tracker is used to pick a horizon 810 corresponding to a location815 that corresponds to the target horizon 215 in FIG. 2. In the exampleshown in FIG. 8, the identified horizon 810 is significantly shorterthan the identified horizon 410 in FIG. 4.

As shown in FIGS. 3-8, each identified horizon 310, 410, 510, 610, 710,and 810 or part of a horizon has a distinct mono-frequency at which thehorizon is smooth, continuous, and easy to pick. The frequency thatgives rise to the largest continuity along the target horizon 215 is 20Hz in this case among the recorded seismic frequency bandwidth rangingfrom 10 Hz to 60 Hz. As such, 20 Hz can be selected as the singlefrequency that gives rise to a predetermined continuity (that is, thelargest continuity) for the target horizon. The single frequency can beused by the example method 150 for a more complete and accurate seismicanalysis of the subterranean region. Referring back to FIG. 1B, from154, method 150 proceeds to 156.

At 156, the seismic data is filtered to mono-frequency seismic data atthe single frequency. The seismic data (for example, the stacked seismicvolume) includes the multiple frequency components, whereas themono-frequency seismic data includes a single-frequency component at thesingle frequency. The seismic data can be filtered, for example, by thedata processing apparatus using existing frequency filters such asfrequency pass filters. From 156, method 150 proceeds to 158.

At 158, a horizon corresponding to the target horizon is identifiedbased on the mono-frequency seismic data. The horizon can be identified,for example, manually or automatically in a manner similar to ordifferent from what is described previously. For example, the horizoncan be identified by the data processing apparatus that runs an autopicker (for example, as a module of a seismic interpretation software).The auto picker can identify the horizon based on the mono-frequencyseismic data, for example, according to one or more existing autohorizon picking algorithms as described previously in connection withthe 2D and 3D auto pickers.

In some implementations, different target horizons (for example, targethorizons located in different areas of interest of the subterraneanregion) may correspond to different single frequencies. In someimplementations, steps 152 and 156 can be repeated to identify singlefrequencies that give rise to respective predetermined continuitycorresponding to different targets horizons. Then, the horizonscorresponding to the different target horizons can be identified basedon respective mono-frequency seismic data at respective singlefrequencies. From 158, method 150 proceeds to 160.

At 160, the identified horizon corresponding to the target horizon isoutput for further analysis and interpretation of the subterraneanregion. The further analysis and interpretation of the subterraneanregion use the identified horizon. The identified horizon can be outputby the data processing apparatus. In some implementations, the dataprocessing apparatus outputs the identified horizon by displaying theidentified horizon on an image of the seismic data (for example, thefull stack seismic volume) for a user's review and analysis through aGUI on a display device associated with the data processing apparatus.In some implementations, the data processing apparatus outputs theidentified horizon by saving the identified horizon on acomputer-readable medium.

In some implementations, the data processing apparatus outputs theidentified horizon by outputting the identified horizon to anothermodule of a seismic interpretation system for determining geologicalfeatures of the subterranean region based on the identified horizon. Forexample, geological features of the subterranean region can bedetermined based on the identified horizon. For example, determininggeological features of the subterranean region based on the identifiedhorizon includes estimating a thickness of a geological layer betweenthe identified horizon and another horizon of the subterranean region.In some implementations, seismic features along the identified horizon,under the identified horizon, or between the identified horizon andanother horizon of the subterranean region can be determined. Theseismic features can be used to map geological features such aschannels, faults, good sands, geological bodies, channels, and areas oflithology environment of depositions, and so on. In someimplementations, the seismic features can be used to estimate thereserve of a reservoir and to locate potential areas to drill in thesubterranean region. After 160, method 150 stops.

FIG. 9 is a block diagram illustrating an example of a computer system900 used to provide computational functionalities associated withdescribed algorithms, methods, functions, processes, flows, andprocedures, according to an implementation of the present disclosure.The illustrated computer 902 is intended to encompass any computingdevice such as a server, desktop computer, laptop/notebook computer,wireless data port, smart phone, personal data assistant (PDA), tabletcomputing device, one or more processors within these devices, anothercomputing device, or a combination of computing devices, includingphysical or virtual instances of the computing device, or a combinationof physical or virtual instances of the computing device. Additionally,the computer 902 can comprise a computer that includes an input device,such as a keypad, keyboard, touch screen, another input device, or acombination of input devices that can accept user information, and anoutput device that conveys information associated with the operation ofthe computer 902, including digital data, visual, audio, similar type ofinformation, or a combination of types of information, on agraphical-type user interface (UI) (or GUI) or other UI.

The computer 902 can serve in a role in a computer system as a client, anetwork component, a server, a database or another persistency, anotherrole, or a combination of roles for performing the subject matterdescribed in the present disclosure. The illustrated computer 902 iscommunicably coupled with a network 930. In some implementations, one ormore components of the computer 902 can be configured to operate withinan environment including cloud-computing-based, local, global, similarenvironment, or a combination of environments.

The computer 902 is an electronic computing device operable to receive,transmit, process, store, or manage data and information associated withthe described subject matter. According to some implementations, thecomputer 902 can also include or be communicably coupled with a server,including an application server, e-mail server, web server, cachingserver, streaming data server, similar server, or a combination ofservers.

The computer 902 can receive requests over network 930 (for example,from a client software application executing on another computer 902)and respond to the received requests by processing the received requestsusing a software application or a combination of software applications.In addition, requests can also be sent to the computer 902 from internalusers (for example, from a command console or by another internal accessmethod), external or third-parties, or other entities, individuals,systems, or computers.

Each of the components of the computer 902 can communicate using asystem bus 903. In some implementations, any or all of the components ofthe computer 902, including hardware, software, or a combination ofhardware and software, can interface over the system bus 903 using anapplication programming interface (API) 912, a service layer 913, or acombination of the API 912 and service layer 913. The API 912 caninclude specifications for routines, data structures, and objectclasses. The API 912 can be either computer-language independent ordependent and refer to a complete interface, a single function, or evena set of APIs. The service layer 913 provides software services to thecomputer 902 or other components (whether illustrated or not) that arecommunicably coupled to the computer 902. The functionality of thecomputer 902 can be accessible for all service consumers using thisservice layer. Software services, such as those provided by the servicelayer 913, provide reusable, defined functionalities through a definedinterface. For example, the interface can be software written in JAVA,C++, another computing language, or a combination of computing languagesproviding data in extensible markup language (XML) format, anotherformat, or a combination of formats. While illustrated as an integratedcomponent of the computer 902, alternative implementations canillustrate the API 912 or the service layer 913 as stand-alonecomponents in relation to other components of the computer 902 or othercomponents (whether illustrated or not) that are communicably coupled tothe computer 902. Moreover, any or all parts of the API 912 or theservice layer 913 can be implemented as a child or a sub-module ofanother software module, enterprise application, or hardware modulewithout departing from the scope of the present disclosure.

The computer 902 includes an interface 904. Although illustrated as asingle interface 904 in FIG. 9, two or more interfaces 904 can be usedaccording to particular needs, desires, or particular implementations ofthe computer 902. The interface 904 is used by the computer 902 forcommunicating with another computing system (whether illustrated or not)that is communicatively linked to the network 930 in a distributedenvironment. Generally, the interface 904 is operable to communicatewith the network 930 and comprises logic encoded in software, hardware,or a combination of software and hardware. More specifically, theinterface 904 can comprise software supporting one or more communicationprotocols associated with communications such that the network 930 orinterface's hardware is operable to communicate physical signals withinand outside of the illustrated computer 902.

The computer 902 includes a processor 905. Although illustrated as asingle processor 905 in FIG. 9, two or more processors can be usedaccording to particular needs, desires, or particular implementations ofthe computer 902. Generally, the processor 905 executes instructions andmanipulates data to perform the operations of the computer 902 and anyalgorithms, methods, functions, processes, flows, and procedures asdescribed in the present disclosure.

The computer 902 also includes a database 906 that can hold data for thecomputer 902, another component communicatively linked to the network930 (whether illustrated or not), or a combination of the computer 902and another component. For example, database 906 can be an in-memory,conventional, or another type of database storing data consistent withthe present disclosure. In some implementations, database 906 can be acombination of two or more different database types (for example, ahybrid in-memory and conventional database) according to particularneeds, desires, or particular implementations of the computer 902 andthe described functionality. Although illustrated as a single database906 in FIG. 9, two or more databases of similar or differing types canbe used according to particular needs, desires, or particularimplementations of the computer 902 and the described functionality.While database 906 is illustrated as an integral component of thecomputer 902, in alternative implementations, database 906 can beexternal to the computer 902. As illustrated, the database 906 holds thepreviously described seismic data 916.

The computer 902 also includes a memory 907 that can hold data for thecomputer 902, another component or components communicatively linked tothe network 930 (whether illustrated or not), or a combination of thecomputer 902 and another component. Memory 907 can store any dataconsistent with the present disclosure. In some implementations, memory907 can be a combination of two or more different types of memory (forexample, a combination of semiconductor and magnetic storage) accordingto particular needs, desires, or particular implementations of thecomputer 902 and the described functionality. Although illustrated as asingle memory 907 in FIG. 9, two or more memories 907 or similar ordiffering types can be used according to particular needs, desires, orparticular implementations of the computer 902 and the describedfunctionality. While memory 907 is illustrated as an integral componentof the computer 902, in alternative implementations, memory 907 can beexternal to the computer 902.

The application 908 is an algorithmic software engine providingfunctionality according to particular needs, desires, or particularimplementations of the computer 902, particularly with respect tofunctionality described in the present disclosure. For example,application 908 can serve as one or more components, modules, orapplications. Further, although illustrated as a single application 908,the application 908 can be implemented as multiple applications 908 onthe computer 902. In addition, although illustrated as integral to thecomputer 902, in alternative implementations, the application 908 can beexternal to the computer 902.

The computer 902 can also include a power supply 914. The power supply914 can include a rechargeable or non-rechargeable battery that can beconfigured to be either user- or non-user-replaceable. In someimplementations, the power supply 914 can include power-conversion ormanagement circuits (including recharging, standby, or another powermanagement functionality). In some implementations, the power-supply 914can include a power plug to allow the computer 902 to be plugged into awall socket or another power source to, for example, power the computer902 or recharge a rechargeable battery.

There can be any number of computers 902 associated with, or externalto, a computer system containing computer 902, each computer 902communicating over network 930. Further, the term “client,” “user,” orother appropriate terminology can be used interchangeably, asappropriate, without departing from the scope of the present disclosure.Moreover, the present disclosure contemplates that many users can useone computer 902, or that one user can use multiple computers 902.

The forgoing detailed description describes frequency-based horizoninterpretation based on seismic data, and is presented to enable anyperson skilled in the art to make and use the disclosed subject matterin the context of one or more particular implementations. Variousmodifications, alterations, and permutations of the disclosedimplementations can be made and will be readily apparent to those ofordinary skill in the art, and the general principles defined can beapplied to other implementations and applications, without departingfrom the scope of the present disclosure. In some instances, detailsunnecessary to obtain an understanding of the described subject mattercan be omitted so as to not obscure one or more describedimplementations with unnecessary detail and inasmuch as such details arewithin the skill of one of ordinary skill in the art. The presentdisclosure is not intended to be limited to the described or illustratedimplementations, but to be accorded the widest scope consistent with thedescribed principles and features.

Implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Software implementations of the described subjectmatter can be implemented as one or more computer programs, that is, oneor more modules of computer program instructions encoded on a tangible,non-transitory, computer-readable computer-storage medium for executionby, or to control the operation of, data processing apparatus.Alternatively, or additionally, the program instructions can be encodedin/on an artificially generated propagated signal, for example, amachine-generated electrical, optical, or electromagnetic signal that isgenerated to encode information for transmission to a receiver apparatusfor execution by a data processing apparatus. The computer-storagemedium can be a machine-readable storage device, a machine-readablestorage substrate, a random or serial access memory device, or acombination of computer-storage mediums. Configuring one or morecomputers means that the one or more computers have installed hardware,firmware, or software (or combinations of hardware, firmware, andsoftware) so that when the software is executed by the one or morecomputers, particular computing operations are performed.

The term “real-time,” “real time,” “realtime,” “real (fast) time (RFT),”“near(ly) real-time (NRT),” “quasi real-time,” or similar terms (asunderstood by one of ordinary skill in the art), means that an actionand a response are temporally proximate such that an individualperceives the action and the response occurring substantiallysimultaneously. For example, the time difference for a response todisplay (or for an initiation of a display) of data following theindividual's action to access the data can be less than 1 millisecond(ms), less than 1 second (s), or less than 5 s. While the requested dataneed not be displayed (or initiated for display) instantaneously, it isdisplayed (or initiated for display) without any intentional delay,taking into account processing limitations of a described computingsystem and time required to, for example, gather, accurately measure,analyze, process, store, or transmit the data.

The terms “data processing apparatus,” “computer,” or “electroniccomputer device” (or equivalent as understood by one of ordinary skillin the art) refer to data processing hardware and encompass all kinds ofapparatus, devices, and machines for processing data, including by wayof example, a programmable processor, a computer, or multiple processorsor computers. The apparatus can also be, or further include specialpurpose logic circuitry, for example, a central processing unit (CPU),an FPGA (field programmable gate array), or an ASIC(application-specific integrated circuit). In some implementations, thedata processing apparatus or special purpose logic circuitry (or acombination of the data processing apparatus or special purpose logiccircuitry) can be hardware- or software-based (or a combination of bothhardware- and software-based). The apparatus can optionally include codethat creates an execution environment for computer programs, forexample, code that constitutes processor firmware, a protocol stack, adatabase management system, an operating system, or a combination ofexecution environments. The present disclosure contemplates the use ofdata processing apparatuses with an operating system of some type, forexample LINUX, UNIX, WINDOWS, MAC OS, ANDROID, IOS, similar operatingsystem, or a combination of operating systems.

A computer program, which can also be referred to or described as aprogram, software, a software application, a unit, a module, a softwaremodule, a script, code, or other component can be written in any form ofprogramming language, including compiled or interpreted languages, ordeclarative or procedural languages, and it can be deployed in any form,including, for example, as a stand-alone program, module, component, orsubroutine, for use in a computing environment. A computer program can,but need not, correspond to a file in a file system. A program can bestored in a portion of a file that holds other programs or data, forexample, one or more scripts stored in a markup language document, in asingle file dedicated to the program in question, or in multiplecoordinated files, for example, files that store one or more modules,sub-programs, or portions of code. A computer program can be deployed tobe executed on one computer or on multiple computers that are located atone site or distributed across multiple sites and interconnected by acommunication network.

While portions of the programs illustrated in the various figures can beillustrated as individual components, such as units or modules, thatimplement described features and functionality using various objects,methods, or other processes, the programs can instead include a numberof sub-units, sub-modules, third-party services, components, libraries,and other components, as appropriate. Conversely, the features andfunctionality of various components can be combined into singlecomponents, as appropriate. Thresholds used to make computationaldeterminations can be statically, dynamically, or both statically anddynamically determined.

Described methods, processes, or logic flows represent one or moreexamples of functionality consistent with the present disclosure and arenot intended to limit the disclosure to the described or illustratedimplementations, but to be accorded the widest scope consistent withdescribed principles and features. The described methods, processes, orlogic flows can be performed by one or more programmable computersexecuting one or more computer programs to perform functions byoperating on input data and generating output data. The methods,processes, or logic flows can also be performed by, and apparatus canalso be implemented as, special purpose logic circuitry, for example, aCPU, an FPGA, or an ASIC.

Computers for the execution of a computer program can be based ongeneral or special purpose microprocessors, both, or another type ofCPU. Generally, a CPU will receive instructions and data from and writeto a memory. The essential elements of a computer are a CPU, forperforming or executing instructions, and one or more memory devices forstoring instructions and data. Generally, a computer will also include,or be operatively coupled to, receive data from or transfer data to, orboth, one or more mass storage devices for storing data, for example,magnetic, magneto-optical disks, or optical disks. However, a computerneed not have such devices. Moreover, a computer can be embedded inanother device, for example, a mobile telephone, a personal digitalassistant (PDA), a mobile audio or video player, a game console, aglobal positioning system (GPS) receiver, or a portable memory storagedevice.

Non-transitory computer-readable media for storing computer programinstructions and data can include all forms of permanent/non-permanentor volatile/non-volatile memory, media and memory devices, including byway of example semiconductor memory devices, for example, random accessmemory (RAM), read-only memory (ROM), phase change memory (PRAM), staticrandom access memory (SRAM), dynamic random access memory (DRAM),erasable programmable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), and flash memory devices;magnetic devices, for example, tape, cartridges, cassettes,internal/removable disks; magneto-optical disks; and optical memorydevices, for example, digital video disc (DVD), CD-ROM, DVD+/−R,DVD-RAM, DVD-ROM, HD-DVD, and BLURAY, and similar optical memorytechnologies. The memory can store various objects or data, includingcaches, classes, frameworks, applications, modules, backup data, jobs,web pages, web page templates, data structures, database tables,repositories storing dynamic information, or similar appropriateinformation including any parameters, variables, algorithms,instructions, rules, constraints, or references. Additionally, thememory can include similar appropriate data, such as logs, policies,security or access data, or reporting files. The processor and thememory can be supplemented by, or incorporated in, special purpose logiccircuitry.

To provide for interaction with a user, implementations of the subjectmatter described in this specification can be implemented on a computerhaving a display device, for example, a CRT (cathode ray tube), LCD(liquid crystal display), LED (Light Emitting Diode), or plasma monitor,for displaying information to the user and a keyboard and a pointingdevice, for example, a mouse, trackball, or trackpad by which the usercan provide input to the computer. Input can also be provided to thecomputer using a touchscreen, such as a tablet computer surface withpressure sensitivity, a multi-touch screen using capacitive or electricsensing, or another type of touchscreen. Other types of devices can beused to interact with the user. For example, feedback provided to theuser can be any form of sensory feedback (such as, visual, auditory,tactile, or a combination of feedback types). Input from the user can bereceived in any form, including acoustic, speech, or tactile input. Inaddition, a computer can interact with the user by sending documents toand receiving documents from a client computing device that is used bythe user (for example, by sending web pages to a web browser on a user'smobile computing device in response to requests received from the webbrowser).

The term “graphical user interface,” or “GUI,” can be used in thesingular or the plural to describe one or more graphical user interfacesand each of the displays of a particular graphical user interface.Therefore, a GUI can represent any graphical user interface, includingbut not limited to, a web browser, a touch screen, or a command lineinterface (CLI) that processes information and efficiently presents theinformation results to the user. In general, a GUI can include aplurality of user interface (UI) elements, some or all associated with aweb browser, such as interactive fields, pull-down lists, and buttons.These and other UI elements can be related to or represent the functionsof the web browser.

Implementations of the subject matter described in this specificationcan be implemented in a computing system that includes a back-endcomponent, for example, as a data server, or that includes a middlewarecomponent, for example, an application server, or that includes afront-end component, for example, a client computer having a graphicaluser interface or a Web browser through which a user can interact withan implementation of the subject matter described in this specification,or any combination of one or more such back-end, middleware, orfront-end components. The components of the system can be interconnectedby any form or medium of wireline or wireless digital data communication(or a combination of data communication), for example, a communicationnetwork. Examples of communication networks include a local area network(LAN), a radio access network (RAN), a metropolitan area network (MAN),a wide area network (WAN), Worldwide Interoperability for MicrowaveAccess (WIMAX), a wireless local area network (WLAN) using, for example,802.11 a/b/g/n or 802.20 (or a combination of 802.11x and 802.20 orother protocols consistent with the present disclosure), all or aportion of the Internet, similar communication network, or a combinationof communication networks. The communication network can communicatewith, for example, Internet Protocol (IP) packets, Frame Relay frames,Asynchronous Transfer Mode (ATM) cells, voice, video, data, or similarinformation between network addresses.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinvention or on the scope of what can be claimed, but rather asdescriptions of features that can be specific to particularimplementations of particular inventions. Certain features that aredescribed in this specification in the context of separateimplementations can also be implemented, in combination, in a singleimplementation. Conversely, various features that are described in thecontext of a single implementation can also be implemented in multipleimplementations, separately, or in any sub-combination. Moreover,although previously described features can be described as acting incertain combinations and even initially claimed as such, one or morefeatures from a claimed combination can, in some cases, be excised fromthe combination, and the claimed combination can be directed to asub-combination or variation of a sub-combination.

Particular implementations of the subject matter have been described.Other implementations, alterations, and permutations of the describedimplementations are within the scope of the following claims as will beapparent to those skilled in the art. While operations are depicted inthe drawings or claims in a particular order, this should not beunderstood as requiring that such operations be performed in theparticular order shown or in sequential order, or that all illustratedoperations be performed (some operations can be considered optional), toachieve desirable results. In certain circumstances, multitasking orparallel processing (or a combination of multitasking and parallelprocessing) can be advantageous and performed as deemed appropriate.

Moreover, the separation or integration of various system modules andcomponents in the previously described implementations should not beunderstood as requiring such separation or integration in allimplementations, and it should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products.

Accordingly, the previously described example implementations do notdefine or constrain the present disclosure. Other changes,substitutions, and alterations are also possible without departing fromthe spirit and scope of the present disclosure.

Furthermore, any claimed implementation is considered to be applicableto at least a computer-implemented method; a non-transitory,computer-readable medium storing computer-readable instructions toperform the computer-implemented method; and a computer systemcomprising a computer memory interoperably coupled with a hardwareprocessor configured to perform the computer-implemented method or theinstructions stored on the non-transitory, computer-readable medium.

What is claimed is:
 1. A computer-implemented method, comprising:receiving, by a data processing apparatus, seismic data of asubterranean region, the seismic data comprising a plurality offrequency components and having a frequency bandwidth spanning aspectrum in a frequency domain, wherein the seismic data is obtainedfrom a seismic survey conducted of the subterranean region; identifyinga target horizon based on the seismic data, the target horizon being ahorizon to be picked based on the seismic data, the horizon representinga seismic reflector between two geological layers in the subterraneanregion; determining a single frequency from the frequency bandwidth ofthe seismic data that gives rise to a predetermined continuity along thetarget horizon; filtering the plurality of frequency components of theseismic data into the single frequency, thereby generatingmono-frequency seismic data, the mono-frequency seismic data comprisinga single-frequency component at the single frequency; identifying ahorizon corresponding to the target horizon based on the mono-frequencyseismic data; and outputting the identified horizon corresponding to thetarget horizon for determining geological features of the subterraneanregion based on the identified horizon.
 2. The computer-implementedmethod of claim 1, wherein determining a single frequency from thefrequency bandwidth of the seismic data that gives rise to apredetermined continuity along the target horizon comprises: decomposingthe seismic data into a plurality of mono-frequency seismic data at aplurality of frequencies; identifying a plurality of respective horizonsbased on the plurality of mono-frequency seismic data; determining outof the plurality of respective horizons a horizon that has thepredetermined continuity along the target horizon; and determining thesingle frequency that gives rise to the horizon that has thepredetermined continuity along the target horizon.
 3. Thecomputer-implemented method of claim 2, wherein the horizon that has thepredetermined continuity along the target horizon comprises a horizonthat has a maximum continuity along the target horizon among theplurality of respective horizons.
 4. The computer-implemented method ofclaim 1, wherein outputting the identified horizon comprises displayingthe identified horizon on an image of the seismic data.
 5. Thecomputer-implemented method of claim 1, wherein identifying a targethorizon based on the seismic data comprises receiving a user's selectionof the target horizon based on the seismic data.
 6. Thecomputer-implemented method of claim 1, comprising determininggeological features of the subterranean region based on the identifiedhorizon.
 7. The computer-implemented method of claim 1, whereindetermining geological features of the subterranean region based on theidentified horizon comprises estimating a thickness of a geologicallayer between the identified horizon and another horizon of thesubterranean region.
 8. A non-transitory, computer-readable mediumstoring one or more instructions executable by a computer system toperform operations comprising: receiving seismic data of a subterraneanregion, the seismic data comprising a plurality of frequency componentsand having a frequency bandwidth spanning a spectrum in a frequencydomain, wherein the seismic data is obtained from a seismic surveyconducted of the subterranean region; identifying a target horizon basedon the seismic data, the target horizon being a horizon to be pickedbased on the seismic data, the horizon representing a seismic reflectorbetween two geological layers in the subterranean region; determining asingle frequency from the frequency bandwidth of the seismic data thatgives rise to a predetermined continuity along the target horizon;filtering the plurality of frequency components of the seismic data intothe single frequency, thereby generating mono-frequency seismic data,the mono-frequency seismic data comprising a single-frequency componentat the single frequency; identifying a horizon corresponding to thetarget horizon based on the mono-frequency seismic data; and outputtingthe identified horizon corresponding to the target horizon fordetermining geological features of the subterranean region based on theidentified horizon.
 9. The non-transitory, computer-readable medium ofclaim 8, wherein determining a single frequency from the frequencybandwidth of the seismic data that gives rise to a predeterminedcontinuity along the target horizon comprises: decomposing the seismicdata into a plurality of mono-frequency seismic data at a plurality offrequencies; identifying a plurality of respective horizons based on theplurality of mono-frequency seismic data; determining out of theplurality of respective horizons a horizon that has the predeterminedcontinuity along the target horizon; and determining the singlefrequency that gives rise to the horizon that has the predeterminedcontinuity along the target horizon.
 10. The non-transitory,computer-readable medium of claim 9, wherein the horizon that has thepredetermined continuity along the target horizon comprises a horizonthat has a maximum continuity along the target horizon among theplurality of respective horizons.
 11. The non-transitory,computer-readable medium of claim 8, wherein outputting the identifiedhorizon comprises displaying the identified horizon on an image of theseismic data.
 12. The non-transitory, computer-readable medium of claim8, wherein identifying a target horizon based on the seismic datacomprises receiving a user's selection of the target horizon based onthe seismic data.
 13. The non-transitory, computer-readable medium ofclaim 8, wherein the operations further comprises determining geologicalfeatures of the subterranean region based on the identified horizon. 14.The non-transitory, computer-readable medium of claim 8, whereindetermining geological features of the subterranean region based on theidentified horizon comprises estimating a thickness of a geologicallayer between the identified horizon and another horizon of thesubterranean region.
 15. A computer-implemented system, comprising: oneor more computers; and one or more computer memory devices interoperablycoupled with the one or more computers and having tangible,non-transitory, machine-readable media storing one or more instructionsthat, when executed by the one or more computers, perform operationscomprising: receiving seismic data of a subterranean region, the seismicdata comprising a plurality of frequency components and having afrequency bandwidth spanning a spectrum in a frequency domain, whereinthe seismic data is obtained from a seismic survey conducted of thesubterranean region; identifying a target horizon based on the seismicdata, the target horizon being a horizon to be picked based on theseismic data, the horizon representing a seismic reflector between twogeological layers in the subterranean region; determining a singlefrequency from the frequency bandwidth of the seismic data that givesrise to a predetermined continuity along the target horizon; filteringthe plurality of frequency components of the seismic data into thesingle frequency, thereby generating mono-frequency seismic data, themono-frequency seismic data comprising a single-frequency component atthe single frequency; identifying a horizon corresponding to the targethorizon based on the mono-frequency seismic data; and outputting theidentified horizon corresponding to the target horizon for determininggeological features of the subterranean region based on the identifiedhorizon.
 16. The computer-implemented system of claim 15, whereindetermining a single frequency from the frequency bandwidth of theseismic data that gives rise to a predetermined continuity along thetarget horizon comprises: decomposing the seismic data into a pluralityof mono-frequency seismic data at a plurality of frequencies;identifying a plurality of respective horizons based on the plurality ofmono-frequency seismic data; determining out of the plurality ofrespective horizons a horizon that has the predetermined continuityalong the target horizon; and determining the single frequency thatgives rise to the horizon that has the predetermined continuity alongthe target horizon.
 17. The computer-implemented system of claim 16,wherein the horizon that has the predetermined continuity along thetarget horizon comprises a horizon that has a maximum continuity alongthe target horizon among the plurality of respective horizons.
 18. Thecomputer-implemented system of claim 15, wherein outputting theidentified horizon comprises displaying the identified horizon on animage of the seismic data.
 19. The computer-implemented system of claim15, wherein identifying a target horizon based on the seismic datacomprises receiving a user's selection of the target horizon based onthe seismic data.
 20. The computer-implemented system of claim 15,wherein the operations further comprises determining geological featuresof the subterranean region based on the identified horizon, whereindetermining geological features of the subterranean region based on theidentified horizon comprises estimating a thickness of a geologicallayer between the identified horizon and another horizon of thesubterranean region.